Data analysis of sunspot time series with SSA and HHT information adaptive methods

Nikolai T. Safiullin; Sergey V. Porshnev; Nathan I. Kleeorin

Data analysis of sunspot time series with SSA and HHT information adaptive methods

Nikolai T. Safiullin, Sergey V. Porshnev, Nathan I. Kleeorin

Department of Mechanical Engineering

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

In the paper, characteristics of the monthly Wolf numbers time series data, containing information about individual solar cycles, are investigated by decomposition into components with two time series methods, namely, the Singular Spectrum Analysis (SSA) and Huang- Hilbert Transform (HHT). These methods do not require any a priori knowledge about analyzed data, making them information adaptive. As a result, some of the known cycles such as Schwabe-Wolf Cycle, Hale Cycle, Gleisberg Cycle, and Suess Cycle have been identified. These components and their properties are compared with each other, as well as with known characteristics of sunspot cycles.

Original language	English
Pages (from-to)	110-119
Number of pages	10
Journal	CEUR Workshop Proceedings
Volume	2005
State	Published - 1 Jan 2017
Event	2nd International Workshop on Radio Electronics and Information Technologies, REIT 2 2017 - Yekaterinburg, Russian Federation Duration: 15 Nov 2017 → …

Keywords

Data analysis
Empirical mode decomposition
Information handling
Singular spectrum analysis
Sunspot numbers
Time series

ASJC Scopus subject areas

Computer Science (all)

Cite this

@article{d31f251949294ad99907352ed81931c2,

title = "Data analysis of sunspot time series with SSA and HHT information adaptive methods",

abstract = "In the paper, characteristics of the monthly Wolf numbers time series data, containing information about individual solar cycles, are investigated by decomposition into components with two time series methods, namely, the Singular Spectrum Analysis (SSA) and Huang- Hilbert Transform (HHT). These methods do not require any a priori knowledge about analyzed data, making them information adaptive. As a result, some of the known cycles such as Schwabe-Wolf Cycle, Hale Cycle, Gleisberg Cycle, and Suess Cycle have been identified. These components and their properties are compared with each other, as well as with known characteristics of sunspot cycles.",

keywords = "Data analysis, Empirical mode decomposition, Information handling, Singular spectrum analysis, Sunspot numbers, Time series",

author = "Safiullin, {Nikolai T.} and Porshnev, {Sergey V.} and Kleeorin, {Nathan I.}",

year = "2017",

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language = "English",

volume = "2005",

pages = "110--119",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

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note = "2nd International Workshop on Radio Electronics and Information Technologies, REIT 2 2017 ; Conference date: 15-11-2017",

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TY - JOUR

T1 - Data analysis of sunspot time series with SSA and HHT information adaptive methods

AU - Safiullin, Nikolai T.

AU - Porshnev, Sergey V.

AU - Kleeorin, Nathan I.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In the paper, characteristics of the monthly Wolf numbers time series data, containing information about individual solar cycles, are investigated by decomposition into components with two time series methods, namely, the Singular Spectrum Analysis (SSA) and Huang- Hilbert Transform (HHT). These methods do not require any a priori knowledge about analyzed data, making them information adaptive. As a result, some of the known cycles such as Schwabe-Wolf Cycle, Hale Cycle, Gleisberg Cycle, and Suess Cycle have been identified. These components and their properties are compared with each other, as well as with known characteristics of sunspot cycles.

AB - In the paper, characteristics of the monthly Wolf numbers time series data, containing information about individual solar cycles, are investigated by decomposition into components with two time series methods, namely, the Singular Spectrum Analysis (SSA) and Huang- Hilbert Transform (HHT). These methods do not require any a priori knowledge about analyzed data, making them information adaptive. As a result, some of the known cycles such as Schwabe-Wolf Cycle, Hale Cycle, Gleisberg Cycle, and Suess Cycle have been identified. These components and their properties are compared with each other, as well as with known characteristics of sunspot cycles.

KW - Data analysis

KW - Empirical mode decomposition

KW - Information handling

KW - Singular spectrum analysis

KW - Sunspot numbers

KW - Time series

UR - http://www.scopus.com/inward/record.url?scp=85037535984&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85037535984

SN - 1613-0073

VL - 2005

SP - 110

EP - 119

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 2nd International Workshop on Radio Electronics and Information Technologies, REIT 2 2017

Y2 - 15 November 2017

ER -

Data analysis of sunspot time series with SSA and HHT information adaptive methods

Abstract

Keywords

ASJC Scopus subject areas

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